Combine narrative with numbers for effective storytelling in R

Setup

Link to scenario: https://www.katacoda.com/orm-mfrigaard/scenarios/03-effective-storytelling

The index.json configuration file is set using the following environment:

"environment": {
  "uilayout": "editor-terminal-split"
},
"backend": {
  "imageid": "rlang:3.4"
}

Outline

Below are the 20 steps (plus intro and finish) files in the scenario.

Objectives

The objectives for this scenario are:

recognize the needs of your audience (data literacy, level of expertise, etc.)
check and communicate data quality with stakeholders
identify the correct data visualization (single variable, bivariate, and multivariate graphs) based on the data
incorporate feedback from stakeholders/audience into graphs
design visualizations with the appropriate detail and annotations that inform (and do not mislead) the audience

The learners

The learners I’m expecting to be participating in this course are:

Jessica will take this class on her own time for professional development.
Peter will this course in a two-day weekend because he needs to complete a project.
Bruce is using these scenarios to supplement a semester-long undergraduate course on R.
Jane has been told to take this course for his job because his team is using R.

intro

included in intro.md?

Welcome!

Welcome to ‘Combine narrative with numbers for effective storytelling in R’! In this scenario, we will cover how to build data visualizations that effectively communicate and engage your audience.

Now that we have some experience with data wrangling with tidyr and dplyr, and data visualization with ggplot2, we can put these tools together to get your point across to stakeholders and audiences.

What makes a bad graphic?

Bad graphs aren’t just ugly; they’re misleading. A chart can have pretty colors and novel font styling, but that can’t make up for inaccurately presenting data. Mislabeling axes, using inappropriate scales or labels, and including unnecessary elements (‘chart junk’) are common characteristics of bad graphics.

We’ll be using the terms ‘graph’, ‘figure,’ and ‘chart’ interchangeably throughout this scenario.

Claus Wilke describes the difference between ugly, bad, and wrong graphs in his excellent text, Fundamental of Data Visualization.

An ugly graph isn’t aesthetic appealing, but the data presented is clear and informative
A bad graph fails to communicate the information it contains because of poor design
A graph is wrong if it’s mathematically incorrect (the underlying calculations, representations, or transformations are inaccurate).

We want to avoid making ugly, bad, and wrong graphs.

What makes a great graphic?

Have you ever heard a joke and thought, “it’s funny because it’s true”?

We all know a great data visualization when we see it, but can you explain why it’s so great? Beyond just being aesthetically appealing (the choice of color palettes, fonts, etc.), data visualizations are tools for communicating complicated information.

Well, great graphics are similar to great jokes in this way–they should reveal a complicated ‘truth’ that was otherwise difficult to comprehend or articulate with words alone.

This scenario will show you how to make sure your graphs and figures communicate complexity effectively without misleading your audience.

step 1

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Things to consider about your audience

You’ll need to determine who the audience or stakeholders will be before creating graphs or figures. You’ll likely create multiple charts throughout a data analysis that you won’t include as a final deliverable. But in most cases, our stakeholders or audience is whoever is getting the final results or product.

The final graphs you produce will depend on 1) the question we’re trying to answer, and 2) the level of statistical literacy of our audience.

If your audience isn’t familiar with particular data visualizations, provide them with enough information to interpret the graph (and check their understanding). However, if you find yourself spending more time explaining a data visualization’s design than what the visualization reveals, we should consider a different graph.

Asking questions

Data visualization should be an iterative process, and getting regular feedback from your audience will help you understand their point of view. It will also help manage their expectations regarding the final deliverable.

As Hilary Mason and D.J. Patil point out in their 2015 text, Data Driven: Creating a Data Culture, asking the right questions “involves domain knowledge and expertise, coupled with a keen ability to see the problem, see the available data, and match up the two.”

The questions below can help guide your project and make sure you understand what your audience/stakeholders are expecting:

What question(s) is this project trying to answer? (or What problem(s) is this project trying to solve?)
Do we have access to the data to answer the question/problem posed? (or do we need to gather more data?)
What is the current format/structure/location of the data? (this will have an enormous impact on the project timeline!)
What context will we present the deliverable(s) in? (slide deck, website, report, etc.)
How familiar will the audience be with the data in our project? (how much background information should we be providing?)

Having the answers to these questions documented will 1) keep your project focused and timely, 2) ensure both you and your client/customer have a clear vision for successful completion.

step 2

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Data lineage: the background on our data

It’s best to start a project off with a ‘view of the forest from outside the trees’. The technical term for this is data lineage, which

“includes the data origin, what happens to it, and where it moves over time.”

Having a “birds” eye view’ of the data ensures there weren’t any problems with exporting or importing. Data lineage also means understanding where the data are coming from–was it collected from an internal relational database, an external vendor, or did it come from the web or social media?

Knowing some of the technical details behind a dataset lets us frame the questions or problems we’re trying to tackle.

Initiate R

Let’s get started! Launch an R console by clicking here -> R (Click on the Run command icon)

Load packages

The package we’ll use to view the entire datasets with R is skimr. We will install and load these packages below:

install.packages(c("tidyverse", "skimr"))
library(tidyverse)
library(skimr)

Navigating Katacoda

Let’s take a quick tour of our Katacoda environment. In the next 18 steps, we’ll be running code, viewing output, and creating graphs. To accomplish this, we’ll need to understand what tools are at our disposal:

Sidebar

You’re reading this in the Sidebar. All of the instructions are in the Sidebar, and at the bottom, you’ll find a “Continue” button to take you to the next step. See the image below:

Code blocks

You will also find code blocks in the Sidebar. All the code blocks will run when you click on them (you’ve already run a few above!). See the image below for more examples:

Terminal

The Terminal is where the R code from each code block will run, along with any text output. We can also use R interactively by typing them directly into the Terminal after the Prompt >.

Go ahead and try it by typing (or copying and pasting) the following commands:

tidyverse::tidyverse_logo()

#> ⬢ __  _    __   .    ⬡           ⬢  . 
#>  / /_(_)__/ /_ ___  _____ _______ ___ 
#> / __/ / _  / // / |/ / -_) __(_-</ -_)
#> \__/_/\_,_/\_, /|___/\__/_/ /___/\__/ 
#>      ⬢  . /___/      ⬡      .       ⬢

You should see the following output in the Terminal:

VSCODE (EXPLORER)

When we create graphs, we will include the ggplot2::ggsave() function in the code block. This function allows us to save the graph image as a .png file in the VSCODE EXPLORER.

VSCODE (ROOT)

Inside the VSCODE EXPLORER, you’ll find a section labeled ROOT. ROOT is a folder that contains our new graph files. We can open the Graph file by clicking on them. See the image below for an example:

The image below gives you an overview of this entire process:

Now that we know how to navigate the Katacoda environment, we can start exploring data and building graphs!

step 3

included in step3.md?

Before you start: what do we expect to see?

Generally speaking, we should have an idea about how many columns and rows the new dataset will contain. We should know some general information about the variable formats, too.

For example, we should see if we’re getting date columns (YYYY-MM-DD), logical (TRUE, FALSE, NA), numerical measurements (integer (1L) or double (1)), or categorical data (character (male and female) or factor (low, medium, high)).

We’re going to load a dataset to demonstrate how to investigate a dataset’s quality or how well it matches our expectations?

These data come from Sean Lahman’s Baseball Database".

Why baseball data?

Now, I am not going to assume everyone participating in this scenario is familiar with baseball. However, this exercise is arguably more rewarding if you are not a baseball fan. If you’re working with data, part of your job to be interested in whatever you’ve been asked to analyze (even if it is only for the monetary reward).

“…if you want to work in data visualisation, you need to be relentlessly and systematically curious. You should try to get interested in anything and everything that comes your way.” - Alberto Cairo, Knight Chair in Visual Journalism, University of Miami

Analyzing and visualizing data you’re not familiar with is a chance to learn something new, and it puts you in a position to ask ‘out of the box’ questions.

Doing your homework

It’s also essential to read any accompanying documentation for new datasets. If we read the documentation on the Lahman website, we find out that People contains “Player names, DOB, and biographical info.” The variables in People are presented below:

People table

playerID = A unique code assigned to each player. The playerID links the data in this file with records in the other files. birthYear = Year player was born
birthMonth = Month player was born
birthDay = Day player was born
birthCountry = Country where player was born
birthState = State where player was born
birthCity = City where player was born deathYear = Year player died
deathMonth = Month player died
deathDay = Day player died
deathCountry = Country where player died
deathState = State where player died
deathCity = City where player died
nameFirst = Player’s first name
nameLast = Player’s last name
nameGiven = Player’s given name (typically first and middle)
weight = Player’s weight in pounds
height = Player’s height in inches
bats = Player’s batting hand (left, right, or both)
throws = Player’s throwing hand (left or right)
debut = Date that player made first major league appearance
finalGame = Date that player made first major league appearance (blank if still active)
retroID = ID used by retrosheet
bbrefID = ID used by Baseball Reference website

Now that we have some background information on this new dataset, we will look at how well People meets our expectations.

Whenever we get a new data source, we should try to view the data in its native format (if possible). We can view the raw data on the Github repository.

Load data

Fortunately, we are also able to load the raw data directly into R using the readr::read_csv() function. We will load the People dataset into R using readr::read_csv(), and assign "https://bit.ly/3scsHw7" to the file argument.

People <- readr::read_csv(file = "https://bit.ly/3scsHw7")

step 4

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Are we seeing what we expected?

Before creating any visualizations, we want a display that gives us an overview of the entire People dataset. This way, we can see if we imported all the variables and observations correctly.

Skimming data

We’ll be using the skimr package. skimr was is designed for:

“displaying summary statistics the user can skim quickly to understand their data”

Below we pass the People dataset to the skimr::skim() function to create PeopleSkim. We then use the base::summary() function to review the new object.

If this code looks unfamiliar to you, review the Introduction to ggplot2 scenario.

PeopleSkim <- People %>%  
  skimr::skim()
summary(PeopleSkim)

Data summary
Name	Piped data
Number of rows	20090
Number of columns	24
_______________________
Column type frequency:
character	14
Date	2
numeric	8
________________________
Group variables	None

The output above shows a high-level summary of all the variables in the People dataset. We can see there are 20090 rows and 24 columns (14 columns are characters, 2 columns are Dates, and 8 are numeric).

Viewing variables by type

The new PeopleSkim object gives us summary information to check against the documentation and help guide our data visualizations. We will start by viewing the variables according to their types in People using skimr::yank() (read the function documentation on Github). The skim_type argument in skimr::yank() takes a variable type ("character", "numeric", or "Date").

Run the code below to use skimr::yank() to view a skim of the character variables in the People dataset.

PeopleSkim %>% 
  skimr::yank(skim_type = "character")

Variable type: character

skim_variable	n_missing	complete_rate	min	max	n_unique
playerID	0	1.00	5	9	20090
birthCountry	61	1.00	3	14	57
birthState	535	0.97	2	22	295
birthCity	174	0.99	3	26	4880
deathCountry	10235	0.49	3	14	25
deathState	10285	0.49	2	20	107
deathCity	10240	0.49	2	26	2676
nameFirst	37	1.00	2	14	2522
nameLast	0	1.00	2	14	10219
nameGiven	37	1.00	2	43	13325
bats	1180	0.94	1	1	3
throws	977	0.95	1	1	3
retroID	56	1.00	8	8	20034
bbrefID	2	1.00	5	9	20088

We can see none of these data are missing (n_missing and complete_rate). Skimr::skim() also shows us the min, max, empty, n_unique, and whitespace for the 14 character values.

Next, we use skimr::yank() to view a skim of the Date variables in the People dataset.

PeopleSkim %>% 
  skimr::yank(skim_type = "Date")

Variable type: Date

skim_variable	n_missing	complete_rate	min	max	median	n_unique
debut	198	0.99	1871-05-04	2020-09-27	1965-04-14	10572
finalGame	198	0.99	1871-05-05	2020-09-27	1971-05-06	9480

The skim of the Date variables shows us which data are missing (n_missing and complete_rate), along with the earliest (min), latest (max), middle (median), and the number of unique (n_unique) of dates.

Do these numbers make sense?

We can use these values for sanity checks. For example, the n_unique for playerID matches the total number of rows in People, which we should expect from the documentation (playerID = “A unique code assigned to each player”). The earliest dates for both debut and finalGame are in May of 1871 (which corresponds to the first MLB game ever played).

Finally, we will use skimr::yank() to view the "numeric" variables in People.

PeopleSkim %>% 
  skimr::yank("numeric")

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
birthYear	114	0.99	1934.42	42.73	1820	1896	1942	1973	2000	▁▅▅▆▇
birthMonth	282	0.99	6.63	3.47	1	4	7	10	12	▇▅▅▆▇
birthDay	424	0.98	15.62	8.76	1	8	16	23	31	▇▇▇▇▆
deathYear	10232	0.49	1966.29	32.95	1872	1943	1968	1993	2020	▁▃▆▇▇
deathMonth	10233	0.49	6.49	3.53	1	3	6	10	12	▇▅▅▅▇
deathDay	10234	0.49	15.53	8.79	1	8	15	23	31	▇▇▇▆▆
weight	817	0.96	187.80	26.33	65	172	185	200	2125	▇▁▁▁▁
height	737	0.96	72.34	2.61	43	71	72	74	83	▁▁▁▇▁

The numeric variables give us some additional information about these 8 columns. Along with n_missing and complete_rate, we get set of summary statistics:

Location statistics

the mean (or average) gives us the expected value for each variable
the median (as p50) or the ‘center’ value for each variable. Half of the values are above, and half are below.

Spread statistics

the lowest value for each variable, or minimum (as p0)
the highest value for each variable, or maximum (as p100)
Together, these two values can give us the range, which is the difference between the maximum and minimum values
the first quartile (as p25), which is the ‘middle’ of the data points below the median
the third quartile (as p75), which is the ‘middle’ of the data points above the median
Together, these two values can give us the interquartile range (IQR), which is the difference between the third and first quartiles
the standard deviation (as sd), a measure of each variable’s disbursement. The standard deviation describes how far a variable’s values are spread out around their mean

Below we use skimr::focus() and skimr::yank() to view the missing, mean, standard deviation, minimum, maximum, and a small histogram for the numeric variables in the People dataset:

PeopleSkim %>% 
  skimr::focus(n_missing, 
               numeric.mean, numeric.sd, 
               numeric.p0, numeric.p100,
               numeric.hist) %>% 
    skimr::yank("numeric")

Variable type: numeric

skim_variable	n_missing	mean	sd	p0	p100	hist
birthYear	114	1934.42	42.73	1820	2000	▁▅▅▆▇
birthMonth	282	6.63	3.47	1	12	▇▅▅▆▇
birthDay	424	15.62	8.76	1	31	▇▇▇▇▆
deathYear	10232	1966.29	32.95	1872	2020	▁▃▆▇▇
deathMonth	10233	6.49	3.53	1	12	▇▅▅▅▇
deathDay	10234	15.53	8.79	1	31	▇▇▇▆▆
weight	817	187.80	26.33	65	2125	▇▁▁▁▁
height	737	72.34	2.61	43	83	▁▁▁▇▁

Do these numbers make sense?

We notice two implausible values from the skimr output: the weight variable maximum value (2125). We can use dplyr’s filter and select functions to find the nameGiven for the abnormally high weight value.

People %>% 
  filter(weight == 2125) %>% 
  select(nameGiven, birthMonth, birthDay, birthYear, weight)

#> # A tibble: 1 x 5
#>   nameGiven    birthMonth birthDay birthYear weight
#>   <chr>             <dbl>    <dbl>     <dbl>  <dbl>
#> 1 Jacob Robert         10       28      1996   2125

Google the player’s name. What is his listed weight on Wikipedia?

As we can see, the majority of the missing values are in the variables with the death prefix (deathDay, deathMonth, and deathYear). The missing values in these variables make sense because, given the lowest birthYear value (1820), we should expect approximately half of the baseball players in the People dataset to be still alive.

Other resources for missing data

Read more about visualizing missing data here and on the visdat package site, or on the inspectdf package website.

step 5

included in step5.md?

Counting values (1)

The skimr output displayed a small histogram for each numeric variable in the People dataset in the previous step. Histograms show the distribution for a single variable.

Load data

These data come from the TidyTuesday project, a data repository who’s intent is

“to provide a safe and supportive forum for individuals to practice their wrangling and data visualization skills independent of drawing conclusions.”

We’re going to use a dataset of Ramen ratings from The Ramen Rater. Read more about these data here.

Below we import the raw data from an external .csv file into Ramen and get a skimr::skim() summary (stored in RamenSkim)

Ramen <- readr::read_csv("https://bit.ly/38sO0S7")
RamenSkim <- skimr::skim(Ramen)

Review data

View the character variables in RamenSkim

RamenSkim %>% 
  skimr::yank("character")

Variable type: character

skim_variable	n_missing	complete_rate	min	max	n_unique
brand	0	1	1	34	456
variety	0	1	3	96	2971
style	2	1	3	10	8
country	0	1	2	13	44

How complete are these data?

View the numeric variables in RamenSkim

RamenSkim %>% 
  skimr::yank("numeric")

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
review_number	1	1	1590.06	917.94	1	795.50	1590.00	2384.5	3180	▇▇▇▇▇
stars	14	1	3.69	1.03	0	3.25	3.75	4.5	5	▁▁▂▇▅

Pay attention to the hist column for stars–it shows the distribution for the values. What are most of the values concentrated?

We will investigate the distribution of stars by building a histogram with ggplot2.

Build a histogram

We’re going to use ggplot2::geom_histogram() to view the distribution the stars variable in Ramen. Note that we are also assigning labels to the graph that includes 1) a clear title, 2) descriptive information about the graph, 3) the source of the data.

# click to execute code
gg_step5_hist_01 <- Ramen %>% 
  ggplot2::ggplot(aes(x = stars)) + 
  ggplot2::geom_histogram() + 
  ggplot2::labs(
       title = "Distribution of ramen stars", 
       subtitle = "bins = 30",
       caption = "source: https://www.theramenrater.com/resources-2/the-list/")
# save
# ggsave(plot = gg_step5_hist_01,
#        filename = "gg-step5-hist-01.png",
#        device = "png",
#        width = 9,
#        height = 6,
#        units = "in")
gg_step5_hist_01

We will need to open the gg-step5-hist-01.png graph in the vscode IDE (above the Terminal console).

Histograms are built by stacking the variable values into a defined set of bins. The default number for bins is 30. We can change the shape of the histogram by changing the bins argument.

Run the code below to see how the distribution looks with 20 bins. Note we also include the color = "white" argument to ensure we can see each bar separately.

# click to execute code
gg_step5_hist_02 <- Ramen %>% 
  ggplot2::ggplot(aes(x = stars)) + 
  ggplot2::geom_histogram(bins = 20, color = "white") + 
  ggplot2::labs(
       title = "Distribution of ramen stars", 
       subtitle = "bins = 20",
       caption = "source: https://www.theramenrater.com/resources-2/the-list/")
# save
# ggsave(plot = gg_step5_hist_02,
#        filename = "gg-step5-hist-02.png",
#        device = "png",
#        width = 9,
#        height = 6,
#        units = "in")
gg_step5_hist_02

Open the gg-step5-hist-02.png graph in the vscode IDE (above the Terminal console).

The stars values fit into 20 bins better than the default 30 because we can see where values are concentrated (and the high frequency of 5-star ratings).

step 6

included in step6.md?

Counting values (2)

The previous step demonstrated how to use a histogram to view the distribution of a single variable. We needed to adjust the bins in the histogram to make its shape easier to interpret. Density plots use kernel smoothing to create cleaner distributions.

Build a density plot

We’re going to use ggplot2::geom_density() to view a density plot of the stars variable in Ramen. We will use fill to color the area underneath the density line with "dodgerblue".

# click to execute code
gg_step6_density_01 <- Ramen %>% 
  ggplot(aes(x = stars)) + 
  geom_density(fill = "dodgerblue") + 
  labs(title = "Distribution of ramen stars", 
  caption = "source: https://www.theramenrater.com/resources-2/the-list/")
# save
# ggsave(plot = gg_step6_density_01,
#        filename = "gg-step6-density-01.png",
#        device = "png",
#        width = 9,
#        height = 6,
#        units = "in")
gg_step6_density_01

Open the gg-step6-density-01.png graph in the vscode IDE (above the Terminal console).

Adding useful labels

Although density plots create a much smoother distribution, the y axis is harder to interpret. To overcome this, we will add two summary statistics programmatically to the labels using the base::paste0() and base::round() functions.

Run the code below to see how this works:

# click to execute code
subtitle_dens_stars <- paste0("Star rating (mean +/- sd): ", 
       # use round() to make sure there are only two decimals
       round(mean(Ramen$stars, na.rm = TRUE), 2),
       " +/- ",
       round(sd(Ramen$stars, na.rm = TRUE), 2))
subtitle_dens_stars

#> [1] "Star rating (mean +/- sd): 3.69 +/- 1.03"

We can now supply subtitle_dens_stars to the labs(subtitle = ) function.

Creating labels this way ensures they are updated whenever the underlying data change.

# click to execute code
gg_step6_density_02 <- Ramen %>% 
  ggplot(aes(x = stars)) + 
  geom_density(fill = "dodgerblue") + 
  labs(title = "Distribution of ramen stars", 
       # combine text with mean() and sd() for stars in Ramen
       subtitle = subtitle_dens_stars,
       # include source
       caption = "source: https://www.theramenrater.com/resources-2/the-list/")
# save
# ggsave(plot = gg_step6_density_02,
#        filename = "gg-step6-density-02.png",
#        device = "png",
#        width = 9,
#        height = 6,
#        units = "in")
gg_step6_density_02

Open the gg-step6-density-02.png graph in the vscode IDE (above the Terminal console).

As we’ve said, an essential part of effective communication is knowing your audience. It’s unlikely these exploratory graphs will be part of our final deliverable, so the audience for these graphs will likely be us!

Using descriptive labels makes sure we know what we’re seeing when we’re viewing our graphs.

step 7

included in step7.md?

Comparing counts

We’ve looked at the distribution of all the values in the stars variable, but what if we were interested in the distribution of stars across the groups in another categorical variable, like style, which is the Style of container (cup, pack, tray, etc.).

We can check the levels of style with dplyr::count()

Ramen %>% dplyr::count(style, sort = TRUE)

#> # A tibble: 9 x 2
#>   style          n
#>   <chr>      <int>
#> 1 Pack        1832
#> 2 Bowl         612
#> 3 Cup          559
#> 4 Tray         138
#> 5 Box           32
#> 6 Restaurant     3
#> 7 <NA>           2
#> 8 Bar            1
#> 9 Can            1

The output above tells us the top five most common reviews for Ramen came from Packs, Bowls, Cups, Trays, and Boxes.

Grouped skims

We can use dplyrs filter, select, and group_by functions with skimr to see the distribution of the stars variable across the five most common style levels.

# click to execute code
Ramen %>% 
  # filter to most common styles
  filter(style %in% c("Pack", "Bowl",
                      "Cup", "Tray", "Box")) %>% 
  # select only stars and style
  dplyr::select(stars, style) %>% 
  # group dataset by style
  dplyr::group_by(style) %>% 
  # skim grouped data
  skimr::skim() %>% 
  # focus on select output
  skimr::focus(n_missing, style,
               numeric.mean, numeric.sd, numeric.hist,
               numeric.p0, numeric.p50, numeric.p100) %>% 
  # only return numeric values
  skimr::yank("numeric")

Variable type: numeric

skim_variable	style	n_missing	mean	sd	p50	p100	hist
stars	Bowl	0	3.71	1.05	3.75	5	▁▁▂▇▆
stars	Box	0	4.21	1.29	5.00	5	▁▁▁▂▇
stars	Cup	0	3.49	1.04	3.50	5	▁▁▂▇▃
stars	Pack	14	3.74	0.99	3.75	5	▁▁▂▇▆
stars	Tray	0	3.60	1.14	3.75	5	▁▁▂▇▆

The output shows Ramen from a Box has the highest stars rating. We are going to confirm this with a ridgeline plot.

The `ggridges` package

The mean and median (p50) in the skimr output tells us the distribution of stars varies slightly for the filtered levels of style, so we will view the density for each distribution with a ridgeline plot from the ggridges package.

Install and load ggridges below:

# click to execute code
install.packages("ggridges")
library(ggridges)

Build labels first!

We’ll build the labels for this graph first in labs_ridge_stars_style, so we know what we’re expecting to see.

# click to execute code
labs_ridge_stars_style <- labs(
       title = "Star ratings by style",  
       subtitle = "Star rating across most common ramen containers",
       caption = "source: https://www.theramenrater.com/resources-2/the-list/",
       x = "Star rating", 
       y = NULL)

I’ve found this practice to be very helpful for conceptualizing graphs before I begin building them, which reduces errors and saves time!

Overlapping density plots

The code below uses ggridges::geom_density_ridges() function to build overlapping density plots. In this plot, we map the fill argument to the style variable. We also want to set the guides(fill = ) to FALSE because we’ll have labels on the graph for each level of style.

# # click to execute code
gg_step7_ridge_01 <- Ramen %>%
  # filter to most common styles
  filter(style %in% c("Pack", "Bowl",
                      "Cup", "Tray", "Box")) %>%
  ggplot(aes(x = stars,
             y = style,
             fill = style)) +
  geom_density_ridges() +
  guides(fill = FALSE) +
  # add labels
  labs_ridge_stars_style
# # save
# ggsave(plot = gg_step7_ridge_01,
#        filename = "gg-step7-ridge-01.png",
#        device = "png",
#        width = 9,
#        height = 6,
#        units = "in")
gg_step7_ridge_01

Open the gg-step7-ridge-01.png graph in the vscode IDE (above the Terminal console).

From the ridgeline plot, we can see that the star ratings for the Box level in style are concentrated around 5.

step 8

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Visualizing relationships

Now that we know how to explore variable distributions, we will look at relationships between two (or more) variables.

The most common graph for visualizing the relationship between two numerical variables is the scatterplot.

A scatterplot displays data for two numeric variables, with one variable on each axis. These can help us detect whether a relationship (or correlation) exists between the two variables.

Load data

For this graph, we will load the dplyr::starwars and get a skimr of these data. These data come from the Star Wars API. Read more about this dataset on dplyrs website.

# click to execute code
StarWars <- dplyr::starwars 
SWSkim <- StarWars %>% skimr::skim()

We’re only interested in the numeric variables StarWars for now, so we’re going to check these columns in SWSkim:

# click to execute code
SWSkim %>% 
  # only return numeric values
  skimr::yank("numeric")

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
height	6	0.93	174.36	34.77	66	167.0	180	191.0	264	▁▁▇▅▁
mass	28	0.68	97.31	169.46	15	55.6	79	84.5	1358	▇▁▁▁▁
birth_year	44	0.49	87.57	154.69	8	35.0	52	72.0	896	▇▁▁▁▁

We can see at least one value of mass that is considerably higher than the rest. We can tell because the location statistics are similar to each other (mean = 97.3, median (p50) = 84.5), but the spread is almost twice the value of the location (sd = 169). The maximum value (p100) of 1358 also confirms this finding.

Labels

We’re going to filter the StarWars data only observations with mass less than 200 (to remove the extreme value out of the data before plotting).

# click to execute code
labs_scatter_ht_mass_01 <- labs(
  title = "Star Wars Character's height and mass", 
  subtitle = "Scatter plot for height and mass (mass < 200)",
  x = "Mass", 
  y = "Height")

Scatterplot (2 variables)

We will create a scatterplot with ggplot2::geom_point(), map mass to the x axis, and map height to the y axis.

# click to execute code
gg_step8_scatter_01 <- StarWars %>% 
  filter(mass < 200) %>% 
  ggplot(aes(x = mass, y = height)) + 
  geom_point() + 
  # add labels
  labs_scatter_ht_mass_01
# save
# ggsave(plot = gg_step8_scatter_01,
#        filename = "gg-step8-scatter-01.png",
#        device = "png",
#        width = 9,
#        height = 6,
#        units = "in")
gg_step8_scatter_01

Open the gg-step8-scatter-01.png graph in the vscode IDE (above the Terminal console).

Based on the scatterplot, we can see a positive relationship between mass and height for Star Wars characters. But is this the same for all types of characters? For example, does this relationship hold for all levels of gender?

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Counting with tabyls

Let’s view the count of gender below using the tabyl() function from the janitor package.

Install and load the janitor package below:

# click to execute code
install.packages("janitor")
library(janitor)

janitor::tabyl() works similar to dplyr::count(), but automatically prints a bit more information in the output. Click on the code block below to create a tably for the gender variable.

# click to execute code
StarWars %>% 
  janitor::tabyl(gender)

#>     gender  n    percent valid_percent
#>   feminine 17 0.19540230     0.2048193
#>  masculine 66 0.75862069     0.7951807
#>       <NA>  4 0.04597701            NA

We can see the standard output produces a percent and valid_percent columns.Read more about the tabyl function options here.

Scatterplot (3 variables)

One way to include the gender variable in the scatterplot is to map it to the color aesthetic. The output from tabyl tells us there are 4 missing values in gender, so we will also filter these out of the plot.

We will update our labels and add gender to the scatterplot in the code below.

# click to execute code
labs_scatter_ht_mass_02 <- labs(
  title = "Star Wars Character's gender, height and mass", 
  subtitle = "Data for gender (feminine/masculine), height, and mass < 200",
  x = "Mass", 
  color = "Gender",
  y = "Height")

gg_step9_scatter_02 <- StarWars %>% 
  filter(!is.na(gender) & mass < 200) %>% 
  ggplot(aes(x = mass, y = height, color = gender)) + 
  geom_point() +
  # add labels
  labs_scatter_ht_mass_02
# save
# ggsave(plot = gg_step9_scatter_02,
#        filename = "gg-step9-scatter-02.png",
#        device = "png",
#        width = 9,
#        height = 6,
#        units = "in")
gg_step9_scatter_02

Open the gg-step9-scatter-01.png graph in the vscode IDE (above the Terminal console).

The color of the points shows that the feminine characters occupy a smaller range of values for the relationship between mass and height.

step 10

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Combining narrative and numbers

Now that we’ve created a few graphs, we should stop and consider what narrative information to include with our displayed numbers.

The text to accompany your graphs will largely depend on the context of the problem you’re trying to solve (or question you’re trying to answer), but there are a few general guidelines we can apply to each type of graph.

Communcation (labels)

Titles should be objective and neutral, expressing the “who,” “what,” and “where” of the figure’s measurements. Avoid jargon and unnecessary descriptive words. Stick with 1) what was measured, 2) when it was measured, and 3) how it was measured (i.e., the units).

When you are building labels, plan on providing enough information that the chart becomes a ‘stand-alone product.’ By this, we mean that if a new observer viewed your graph, they would at minimum be able to understand what point the figure was trying to make (i.e., “this graph shows the values in X variable,” or “this figure shows the relationship between X and Y”).

Communication (distributions)

You aren’t likely to include the variable distribution graphs in your final deliverable, but they provide essential information for your audience. For example, single-variable plots can tell us if the data had any outliers (or extreme values). Histograms, density, and ridgeline plots can also tell us if a variable has a normal distribution, which is a crucial assumption to check before modeling. Summary statistics are also vital to include (usually in a table) because it tethers the figure to mathematical values.

The information from these exploratory charts gives your narrative context and frames the problem. If we were telling a story, this would be the portion that tells us the setting or universe in which our characters live.

Communication (relationships)

Describing a relationship answers a certain kind of question, i.e., “What is the relationship between two quantitative measures?” When presenting a graph with relationships, consider the context and framing for the conclusions your audience will draw. Is this good news? For example, if the chart displays a drop in sales over time, anticipate how this will change your presentation’s tone, and be ready to answer questions.

It’s also important not to confuse your audience when designing graphs. Relationships with ‘good news’ should have the data points showing a positive trend (i.e., as X values increase, so do Y values), and vice-versa. You don’t want to find yourself in a situation where you’re explaining that your graph doesn’t show what you’re audience is seeing.

step 11

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step 12

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step 13

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step 14

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step 15

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step 16

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step 17

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step 18

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step 19

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finish

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Combine narrative with numbers for effective storytelling in R

Martin Frigaard

Setup

Outline

Objectives

The learners

intro

Welcome!

What makes a bad graphic?

What makes a great graphic?

step 1

Things to consider about your audience

Asking questions

step 2

Data lineage: the background on our data

Initiate R

Load packages

Navigating Katacoda

step 3

Before you start: what do we expect to see?

Why baseball data?

Doing your homework

Load data

step 4

Are we seeing what we expected?

Skimming data

Viewing variables by type

Other resources for missing data

step 5

Counting values (1)

Load data

Review data

Build a histogram

step 6

Counting values (2)

Build a density plot

Adding useful labels

step 7

Comparing counts

Grouped skims

The ggridges package

Build labels first!

Overlapping density plots

step 8

Visualizing relationships

Load data

Labels

Scatterplot (2 variables)

step 9

Counting with tabyls

Scatterplot (3 variables)

step 10

Combining narrative and numbers

Communcation (labels)

Communication (distributions)

Communication (relationships)

step 11

step 12

step 13

step 14

step 15

step 16

step 17

step 18

step 19

step 20

finish

The `ggridges` package